Precision targeting using machine learning to optimize medication therapy management delivery in value-based care: A quasi-experimental study using real-world evidence
In brief
Machine-learning targeting linked to 21% pharmacist-call acceptance, up from 13%
In a matched analysis of nearly 184,000 patient pairs, machine-learning targeting was associated with an 8.6 percentage-point increase in acceptance of pharmacist consultations compared with traditional eligibility-based assignment. Because the study compared different years rather than randomly assigning patients, it cannot establish that the targeting model caused the improvement; whether results hold across other pharmacy programs remains unknown.
- Journal
- Journal of managed care & specialty pharmacy (Q1)
- Published
- 1 November 2026
- Study design
- Non-randomized / quasi-experimental trial
- Evidence level
- Level 2, Moderate (CEBM 2b)
- Authors
- Mouna Zamran, Heather Kirkham, Travis Henschel, Edward A Witt, Peggy Wonders, Norman W Gill
- PMID
- 42925248
- DOI
- 10.18553/jmcp.2026.32.11.1368
Why clinicians should know about it
- Picked for Health Informatics (paper of the day, 26 October 2026): Real-world ML integration measurably changes pharmacist outreach workflow
Abstract
BACKGROUND: Medication therapy management (MTM) is a core component of managed care pharmacy that improves medication safety, adherence, and clinical outcomes in high-risk populations. However, traditional eligibility criteria lack precision, limiting scalability and impact. OBJECTIVE: To provide real-world evidence of how machine learning (ML) integration into pharmacy workflows can improve MTM delivery by identifying patients most likely to engage. METHODS: We conducted a retrospective cohort study using a de-identified pharmacy dataset. MTM-eligible patients in 2025 were assigned to centralized or community pharmacists via an ML-based precision-targeting model, whereas a 2024 cohort was assigned using traditional eligibility-based assignment. The propensity score matching model incorporated demographics, medication patterns, social determinants of health, engagement history, and plan-level/store-level characteristics. The primary outcome was the call favorability rate (acceptance of a pharmacist consultation). RESULTS: After propensity score matching, 183,888 matched patient pairs were analyzed. The ML cohort showed a 21.2% call favorability rate vs 12.6% in the historical cohort, an 8.6% absolute increase and 68% relative improvement (risk ratio = 1.68). Results were robust to moderate unmeasured confounding (E value = 2.75). Results were statistically significant (P < 0.001) and remained stable with checks for bias. CONCLUSIONS: ML-based precision targeting was associated with higher MTM call favorability compared with traditional eligibility-based approaches. These findings support integrating ML into pharmacists' workflows to improve patient engagement and resource use amid evolving reimbursement structures. The results were robust to potential hidden bias, suggesting that this approach could be scaled for use in managed care MTM programs.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.